






"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE   =   {
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE   =   ['BlenderbotTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE   =   [
		    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BlenderbotForCausalLM',
		    'BlenderbotForConditionalGeneration',
		    'BlenderbotModel',
		    'BlenderbotPreTrainedModel',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE   =   [
		    'TFBlenderbotForConditionalGeneration',
		    'TFBlenderbotModel',
		    'TFBlenderbotPreTrainedModel',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE   =   [
		    'FlaxBlenderbotForConditionalGeneration',
		    'FlaxBlenderbotModel',
		    'FlaxBlenderbotPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_blenderbot import (
		    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlenderbotConfig,
		    BlenderbotOnnxConfig,
		)
		from .tokenization_blenderbot import BlenderbotTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blenderbot import (
				    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlenderbotForCausalLM,
				    BlenderbotForConditionalGeneration,
				    BlenderbotModel,
				    BlenderbotPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blenderbot import (
				    TFBlenderbotForConditionalGeneration,
				    TFBlenderbotModel,
				    TFBlenderbotPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_blenderbot import (
				    FlaxBlenderbotForConditionalGeneration,
				    FlaxBlenderbotModel,
				    FlaxBlenderbotPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE   =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







"""simple docstring"""


from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass




@dataclass
class 						__UpperCamelCase     :
					lowercase_    :				int
					lowercase_    :				TreeNode | None      =  None
					lowercase_    :				TreeNode | None      =  None


__SCREAMING_SNAKE_CASE   =   namedtuple('CoinsDistribResult', 'moves excess')


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if root is None:
												return 0

						# Validation
						def count_nodes(a__ ) -> int:
												if node is None:
																		return 0

												return count_nodes(node.left ) + count_nodes(node.right ) + 1

						def count_coins(a__ ) -> int:
												if node is None:
																		return 0

												return count_coins(node.left ) + count_coins(node.right ) + node.data

						if count_nodes(a__ ) != count_coins(a__ ):
												raise ValueError('The nodes number should be same as the number of coins' )

						# Main calculation
						def get_distrib(a__ ) -> CoinsDistribResult:

												if node is None:
																		return CoinsDistribResult(0     ,      1 )

												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			get_distrib(node.left )
												lowerCAmelCase	, lowerCAmelCase   :str						=			get_distrib(node.right )

												lowerCAmelCase   :str						=			1 - left_distrib_excess
												lowerCAmelCase   :Optional[Any]						=			1 - right_distrib_excess

												lowerCAmelCase   :Any						=			(
												    left_distrib_moves
												    + right_distrib_moves
												    + abs(a__ )
												    + abs(a__ )
												)
												lowerCAmelCase   :Tuple						=			node.data - coins_to_left - coins_to_right

												return CoinsDistribResult(a__     ,      a__ )

						return get_distrib(a__ )[0]


if __name__ == "__main__":
		import doctest

		doctest.testmod()